Back to Activity1

Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.


Scatterplot Questions

1. Create a scatterplot using average MPG and another category that you feel may influence fuel efficiency. Answer the following questions.

  • Identify the category you chose and why you thought there might be a relationship BEFORE creating the scatterplot?

Answer: We discussed the bigger the engine (more horsepower) the less the fuel efficiency.
  • Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?

Answer: X-axis is the MPG and Y-axis is the horsepower. That was the way it was in order on the spreadsheet
  • Do you believe there is a relationship between the two categories? Why or why not?

Answer: Yes, it appears that the higher the MPG the lower the horsepower.
  • If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?

Answer: It appears to be negative.

Regression Questions

(What is Regression?)

Create the linear regession equation in Excel, which Excel calls the trend line. Click the boxes to create both the equation and the r2 value on the graph. Answer the following questions.

  • What is your regression equation? Explain what the equation means in words.

Answer: y = -6.3623x + 307.5. As you increase MPG, the horsepower decreases.

  • What is your r2 value? Is this a strong correlation? Why or Why not? If you are not sure, try searching the internet for supporting documents. Provide URL's for where you find your information

Answer: R² = 0.4301 No, it is not very close to 1.
  • Based on all the information you have, has your belief about the relationship of the two categories changes? Why or why not?

Answer: No, but today's car manufacturers are doing a better job of increasing fuel efficiency.


Analysis

Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.

  • How would you determine which equation had the best relationship?

Answer: Power
  • Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?

Answer: no, the power equation's R value was closer to 1



Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.



Group_1.jpg